store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_196837', 'seed': 196837, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[22040, 25629, 5552, 26789, 50674, 24602, 37555, 6561, 33889, 8235, 30390, 50985, 37175, 55738, 36205, 9548, 2026, 7924, 25905, 20282]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6714, 'nll': 2.122996425628662}, 'chosen_samples': [2359, 13407, 3332, 19755, 25105, 21425, 9450, 29876, 19833, 51349], 'chosen_samples_score': ['1.1099637', '1.1134048', '1.1162338', '1.1240838', '1.1249039', '1.1892533', '1.1270313', '1.1911784', '1.1537372', '1.1484233']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7215, 'nll': 1.6403329610824584}, 'chosen_samples': [36398, 36589, 42428, 7923, 27335, 53854, 17434, 59418, 57257, 42681], 'chosen_samples_score': ['1.064244', '1.0729151', '1.0770847', '1.1101904', '1.092556', '1.1021719', '1.0794182', '1.113075', '1.1705775', '1.1536592']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7197, 'nll': 1.4557003140449525}, 'chosen_samples': [38639, 19187, 22921, 56121, 54565, 34116, 347, 38760, 47787, 26509], 'chosen_samples_score': ['0.9403937', '0.95316064', '0.9565248', '0.9604525', '0.9832346', '0.9898892', '0.9975', '1.0078733', '1.0328183', '1.0156803']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7519, 'nll': 1.1894285440444947}, 'chosen_samples': [52971, 8924, 33593, 35156, 38038, 48681, 54403, 14193, 44345, 45686], 'chosen_samples_score': ['0.9012215', '0.9059902', '0.9142785', '0.90642333', '0.9085497', '0.91523945', '0.93603945', '0.9525734', '0.9606906', '0.96175665']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7924, 'nll': 1.06944180727005}, 'chosen_samples': [45218, 13203, 9440, 46412, 28920, 22824, 52144, 39620, 8253, 30897], 'chosen_samples_score': ['0.81925374', '0.8205955', '0.82134086', '0.8452151', '0.84562635', '0.826088', '1.0051992', '0.89226866', '0.8848103', '0.8256187']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8034, 'nll': 1.0450676023960113}, 'chosen_samples': [53626, 16302, 5093, 1272, 10244, 27930, 26233, 21295, 11500, 7168], 'chosen_samples_score': ['0.8220677', '0.8249314', '0.8276866', '0.83470803', '0.8564473', '0.9150002', '0.91123575', '0.87030727', '0.86921656', '0.88775337']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7789, 'nll': 1.0805411159992218}, 'chosen_samples': [4678, 10492, 45437, 33697, 43942, 34268, 16017, 47068, 21151, 17728], 'chosen_samples_score': ['0.79163176', '0.7935944', '0.7957511', '0.7991426', '0.7994259', '0.8030624', '0.804222', '0.82329', '0.8089441', '0.9128988']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8559, 'nll': 0.8560690820217133}, 'chosen_samples': [41052, 37050, 4061, 23902, 31545, 32843, 12986, 50223, 28199, 22167], 'chosen_samples_score': ['0.9699136', '0.9701191', '0.9808903', '0.98989123', '1.0937853', '0.98319334', '0.98253196', '1.0503788', '0.98158365', '0.99816936']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8433, 'nll': 0.8817383348941803}, 'chosen_samples': [25384, 41324, 54481, 4955, 14139, 27503, 45256, 49352, 35628, 59390], 'chosen_samples_score': ['0.73015875', '0.73063105', '0.7596566', '0.7314665', '0.77238435', '0.744827', '0.76291066', '0.88580084', '0.75542223', '0.773851']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8629, 'nll': 0.8766009867191314}, 'chosen_samples': [58390, 41371, 35043, 27783, 1447, 31664, 49515, 51614, 14577, 11693], 'chosen_samples_score': ['1.0421517', '1.0595728', '1.0632708', '1.0690637', '1.105679', '1.0814471', '1.1243021', '1.0921664', '1.0739646', '1.1595628']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8678, 'nll': 0.7722893446683884}, 'chosen_samples': [47595, 55396, 22480, 20171, 11208, 27447, 6838, 38389, 42703, 52306], 'chosen_samples_score': ['0.86021703', '0.8610285', '0.88167995', '0.8952662', '0.902267', '0.8671954', '0.88895005', '0.9120089', '0.93290806', '0.9861139']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8873, 'nll': 0.707463976740837}, 'chosen_samples': [16952, 56379, 33388, 24255, 42193, 42638, 16967, 24363, 30139, 18196], 'chosen_samples_score': ['0.9490452', '0.96113485', '0.96480066', '0.96716267', '0.9692641', '0.9751139', '0.97722006', '0.99758035', '1.0632929', '0.976307']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.881, 'nll': 0.6790414333343506}, 'chosen_samples': [29390, 14935, 40766, 27793, 47651, 12663, 14896, 59919, 21012, 25332], 'chosen_samples_score': ['0.8783559', '0.8958016', '0.89803845', '0.9086459', '0.920632', '0.93346494', '0.95546323', '0.9435737', '0.99348027', '1.0053452']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9006, 'nll': 0.5841450393199921}, 'chosen_samples': [6251, 5728, 26444, 17296, 28491, 46126, 23642, 15832, 37048, 32918], 'chosen_samples_score': ['0.87149704', '0.88583016', '0.88674027', '0.90717655', '0.9126928', '0.91734004', '0.9873257', '0.9216451', '0.91762', '1.0745133']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.91, 'nll': 0.5993095129728317}, 'chosen_samples': [50916, 52294, 15450, 2092, 28731, 52115, 43126, 36126, 36821, 2496], 'chosen_samples_score': ['0.97956055', '0.9804038', '0.9818149', '1.0801827', '1.1623392', '1.0033445', '1.0863211', '1.0844975', '1.0454099', '0.98523724']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9226, 'nll': 0.5242416605353355}, 'chosen_samples': [44898, 43288, 53872, 19948, 57334, 23423, 49282, 5000, 57041, 20050], 'chosen_samples_score': ['0.98839724', '0.98986924', '1.0007648', '1.0086601', '1.0091068', '1.0150464', '1.0790374', '1.0258708', '1.0346847', '1.1016557']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9262, 'nll': 0.5029643610119819}, 'chosen_samples': [47328, 49517, 27458, 11482, 46274, 51432, 52914, 54954, 58560, 43560], 'chosen_samples_score': ['0.9832038', '0.98834544', '1.0139487', '1.0457587', '1.0342323', '1.0374954', '1.0140901', '1.0207386', '0.9924427', '1.1187223']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9217, 'nll': 0.5132319003343582}, 'chosen_samples': [52694, 21700, 17478, 11534, 55388, 602, 55042, 37481, 11292, 49824], 'chosen_samples_score': ['0.9017576', '0.9028959', '0.903101', '0.9034901', '0.91332734', '0.93682045', '0.9284064', '0.97843343', '0.9251822', '0.97542244']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9226, 'nll': 0.5143313318490982}, 'chosen_samples': [20187, 1127, 30756, 22531, 49489, 42121, 19590, 32323, 50930, 14317], 'chosen_samples_score': ['0.84597385', '0.850546', '0.8535488', '0.8536382', '0.8570888', '0.88693535', '0.9863456', '0.9852456', '0.92928773', '0.9015385']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9344, 'nll': 0.46027918756008146}, 'chosen_samples': [40576, 2765, 49050, 9535, 52478, 13942, 21333, 17486, 26528, 1608], 'chosen_samples_score': ['0.8508393', '0.85310346', '0.8550005', '0.8698106', '0.90574837', '0.8956267', '0.9095949', '0.88152003', '0.87277746', '0.8965584']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9458, 'nll': 0.3989648580551147}, 'chosen_samples': [43176, 52039, 8419, 36818, 13259, 18487, 21023, 31301, 20989, 3810], 'chosen_samples_score': ['0.9353379', '0.9387681', '0.9495628', '0.95876396', '0.9659433', '0.96795344', '1.2312801', '0.9954988', '1.0392941', '0.97870094']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.942, 'nll': 0.41484375298023224}, 'chosen_samples': [51736, 37469, 49624, 16834, 54950, 15969, 22607, 20169, 9396, 32814], 'chosen_samples_score': ['0.9197397', '0.92669916', '0.93008447', '0.9375335', '0.973418', '0.9451056', '0.99924266', '0.95063746', '0.94362724', '0.95650905']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.945, 'nll': 0.39894364923238756}, 'chosen_samples': [24160, 30900, 41933, 56487, 45800, 50106, 44753, 49242, 42702, 50370], 'chosen_samples_score': ['0.91080207', '0.91748816', '0.9982101', '0.9334002', '0.9963408', '0.92814916', '0.97818387', '0.9155646', '0.9428982', '0.9518976']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9474, 'nll': 0.3980584442615509}, 'chosen_samples': [52392, 41108, 54004, 42415, 2423, 1239, 56495, 18324, 34520, 58536], 'chosen_samples_score': ['0.92340094', '0.9246168', '0.9395139', '0.9440669', '0.970111', '0.97613597', '0.9630525', '0.98003954', '1.1156383', '1.0126524']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9515, 'nll': 0.38435314893722533}, 'chosen_samples': [10202, 25546, 7851, 12594, 56134, 53832, 49656, 12831, 9180, 8447], 'chosen_samples_score': ['0.91250694', '0.9137845', '0.9989714', '0.9274619', '0.96234524', '0.93289495', '0.9233436', '0.99946797', '1.024462', '1.1448216']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9497, 'nll': 0.39692488461732867}, 'chosen_samples': [19546, 9448, 33505, 33338, 38705, 32276, 34328, 7926, 50371, 36686], 'chosen_samples_score': ['0.9080351', '0.9158272', '0.9171737', '0.9162999', '0.94242555', '0.939075', '0.95588404', '0.992857', '1.0350842', '0.98040164']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9522, 'nll': 0.3806259915232658}, 'chosen_samples': [17540, 24943, 9966, 8299, 36744, 20217, 13774, 50840, 47443, 44040], 'chosen_samples_score': ['0.8589486', '0.8628214', '0.86360055', '0.86518115', '0.8833644', '0.9190043', '0.9161764', '1.1150291', '0.8821438', '0.89209634']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9572, 'nll': 0.32096041440963746}, 'chosen_samples': [33353, 42042, 59314, 32776, 35406, 576, 1674, 7886, 49859, 31308], 'chosen_samples_score': ['0.89955413', '0.89998835', '0.98353386', '0.9198871', '0.92781943', '0.9515855', '0.9503773', '0.90978265', '0.9495971', '0.9657721']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.957, 'nll': 0.3603642925620079}, 'chosen_samples': [31224, 20623, 35654, 4185, 18598, 24263, 55128, 207, 40654, 55739], 'chosen_samples_score': ['0.8971882', '0.90184087', '0.9121945', '0.92788935', '0.9332745', '0.92762256', '0.99216455', '1.0355291', '1.0153863', '1.067124']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9606, 'nll': 0.3344646319746971}, 'chosen_samples': [32836, 22083, 52169, 24990, 39320, 59681, 37907, 28293, 53656, 18003], 'chosen_samples_score': ['0.9145087', '0.99573916', '1.0068785', '0.97123367', '1.0984039', '1.0736592', '0.9275809', '0.9407398', '0.9562751', '0.94766617']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9565, 'nll': 0.3550474613904953}, 'chosen_samples': [33161, 22404, 9687, 52808, 55906, 7767, 40106, 6991, 46027, 5536], 'chosen_samples_score': ['0.96060866', '0.9629877', '1.0108263', '1.0115204', '0.9965758', '0.96103', '1.0261413', '1.0270505', '1.0269212', '1.0797398']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9551, 'nll': 0.39223401844501493}, 'chosen_samples': [56014, 6416, 41540, 8771, 50562, 39999, 59294, 40905, 41578, 50878], 'chosen_samples_score': ['0.80418307', '0.85950506', '0.86363465', '0.9633808', '0.8783348', '0.81118774', '0.8384687', '0.810814', '0.8652727', '0.82622683']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9624, 'nll': 0.3167366415262222}, 'chosen_samples': [31347, 31738, 47888, 45602, 1518, 40530, 9559, 32513, 32419, 43783], 'chosen_samples_score': ['0.9755551', '0.9977684', '1.0177426', '1.0240214', '1.0330484', '1.1186328', '1.0702055', '1.1474556', '1.1456654', '1.0413084']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9613, 'nll': 0.33321532905101775}, 'chosen_samples': [5495, 14765, 47220, 57665, 46368, 30451, 4063, 38187, 13149, 39602], 'chosen_samples_score': ['0.8822455', '0.88334733', '0.9204701', '0.9322795', '0.94826597', '0.95414954', '1.0285845', '0.9580342', '1.0414616', '0.9816988']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9632, 'nll': 0.31794560253620147}, 'chosen_samples': [5790, 14722, 40466, 26017, 21355, 5408, 8297, 51261, 43310, 53019], 'chosen_samples_score': ['0.9086541', '0.9116876', '0.9126844', '0.97302926', '0.967161', '0.9279379', '0.9163893', '0.99487215', '0.95710075', '0.93710494']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9646, 'nll': 0.29912597090005877}, 'chosen_samples': [49563, 21426, 27845, 17494, 4153, 15779, 42973, 6289, 31252, 3580], 'chosen_samples_score': ['0.904087', '0.907887', '0.9102792', '0.9556123', '0.9165732', '0.9280319', '0.96971107', '0.98210317', '1.0022181', '1.0132633']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9616, 'nll': 0.31220739483833315}, 'chosen_samples': [36417, 40066, 39942, 36515, 43702, 50424, 57742, 32519, 40702, 27429], 'chosen_samples_score': ['0.8268659', '0.8391389', '0.8461796', '0.8555796', '0.8821412', '0.88958234', '0.919317', '0.9909655', '0.94421875', '0.91419655']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9663, 'nll': 0.2882195249199867}, 'chosen_samples': [34665, 11378, 3030, 52218, 8932, 41295, 15402, 7478, 44095, 12277], 'chosen_samples_score': ['0.8812405', '0.88213813', '0.8824539', '0.8840211', '0.8911813', '0.8899101', '0.94573426', '0.8910786', '0.9470103', '0.89756']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9641, 'nll': 0.31315048336982726}, 'chosen_samples': [33752, 34899, 41196, 41453, 57822, 24479, 29704, 41714, 21150, 48102], 'chosen_samples_score': ['0.9152414', '0.9230507', '0.93717456', '0.93835914', '0.94921935', '0.9523178', '1.0326052', '0.9879996', '0.97568643', '1.0447378']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9667, 'nll': 0.297548808157444}, 'chosen_samples': [19450, 41495, 33812, 50369, 46524, 3719, 29176, 51764, 36072, 712], 'chosen_samples_score': ['0.90045905', '0.9009823', '0.915495', '0.90193236', '0.9163187', '0.94923353', '0.95684576', '0.92256725', '1.0928099', '1.024709']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9713, 'nll': 0.2667548954486847}, 'chosen_samples': [15771, 48933, 1160, 43950, 5216, 340, 54885, 20641, 5295, 18720], 'chosen_samples_score': ['0.9846788', '0.9897707', '1.0156891', '1.0289453', '1.1407633', '1.033773', '1.0503539', '1.1118553', '1.1533518', '1.133306']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9728, 'nll': 0.2571037158370018}, 'chosen_samples': [33598, 42979, 43897, 20945, 8761, 52140, 7182, 27859, 47297, 17039], 'chosen_samples_score': ['0.8643333', '0.8707888', '0.8710373', '0.8720097', '0.8729951', '0.88025904', '0.8804217', '0.9526578', '0.88674015', '0.92472553']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9703, 'nll': 0.27172459959983825}, 'chosen_samples': [3727, 46247, 3094, 59836, 36421, 15743, 50417, 25116, 18864, 31046], 'chosen_samples_score': ['0.9300342', '0.93231213', '0.9412963', '0.94442403', '0.9639036', '0.96949786', '1.0720674', '0.9875905', '1.0383874', '1.0206928']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9687, 'nll': 0.27561596482992173}, 'chosen_samples': [22139, 20660, 34902, 39561, 10256, 49192, 4822, 51964, 42734, 42472], 'chosen_samples_score': ['0.87924135', '0.88517416', '0.8944085', '0.9215758', '0.9045749', '0.97744536', '0.91044986', '0.89915174', '0.9097922', '0.9200493']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.975, 'nll': 0.2588894568383694}, 'chosen_samples': [20792, 48355, 13969, 49733, 35102, 32880, 52462, 24462, 5298, 3980], 'chosen_samples_score': ['0.94797045', '0.95043707', '0.97386384', '0.97100425', '0.9805888', '1.0304779', '1.007395', '1.0221237', '1.1466017', '1.1947012']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9717, 'nll': 0.2771345928311348}, 'chosen_samples': [22759, 25306, 37147, 44172, 20820, 3136, 4164, 20016, 39208, 36704], 'chosen_samples_score': ['0.85949624', '0.8598821', '0.873171', '0.87850916', '0.88736355', '0.9393268', '0.9591234', '0.8895761', '0.8850321', '1.0015371']})
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store['iterations'].append({'num_epochs': 18, 'test_metrics': {'accuracy': 0.9823, 'nll': 0.185596564412117}, 'chosen_samples': [46088, 44442, 17466, 50239, 35482, 52688, 34500, 6466, 55190, 30751], 'chosen_samples_score': ['0.8160917', '0.8178619', '0.86634094', '0.84110546', '0.8204658', '0.8245831', '0.8767871', '0.89929163', '0.92244613', '0.99855006']})
store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9829, 'nll': 0.2080754891037941}, 'chosen_samples': [41349, 46440, 27176, 23104, 892, 13998, 2862, 49354, 9431, 47870], 'chosen_samples_score': ['0.8584344', '0.87853706', '0.88577074', '0.89638823', '0.9031926', '0.9352164', '0.93108535', '0.9047189', '0.9363793', '0.90654117']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9826, 'nll': 0.20503056049346924}, 'chosen_samples': [25482, 52927, 9118, 52938, 37750, 59401, 22597, 11613, 4850, 17079], 'chosen_samples_score': ['0.730459', '0.7339237', '0.73447466', '0.7468844', '0.75089324', '0.75116694', '0.75865644', '0.80348957', '0.9317027', '0.77092797']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9846, 'nll': 0.18526357635855675}, 'chosen_samples': [13428, 30764, 26266, 1033, 262, 24589, 20720, 50462, 44258, 49889], 'chosen_samples_score': ['0.84130174', '0.84363246', '0.84365195', '0.8535256', '0.8670791', '0.9862747', '0.8760854', '0.8876111', '0.91503215', '0.9220413']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9844, 'nll': 0.19697981774806977}, 'chosen_samples': [34188, 31742, 52138, 30770, 14790, 24278, 40046, 52899, 1302, 2064], 'chosen_samples_score': ['0.705504', '0.70849174', '0.71566516', '0.76589113', '0.7866302', '0.7543734', '0.7654463', '0.74912155', '0.7437094', '0.71977633']})
